Identifying genetic regulations on immune cell type proportions and their impacts on autoimmune diseases

Genetic regulation of immune cell composition plays a crucial role in the etiology of complex diseases, yet remains poorly understood. We propose a unified analytical framework that integrates genome-wide association studies (GWAS) of cell type proportions with cell-type-wide association studies (cWAS) to systematically characterize both the genetic regulation of immune cell composition and its downstream effects on disease risk. Using single-cell RNA sequencing data from the OneK1K cohort, we conducted a GWAS of immune cell-type proportions with a depth-weighted quasi-binomial model designed for bounded, overdispersed traits. We identified 47 genome-wide significant loci influencing eight fine-labeled immune cell subtypes. Leveraging these identified genetic effects, we further imputed genetically regulated proportions (GRPs) using polygenic risk score (PRS)-based imputation and assessed their associations with complex diseases through cWAS. We identified five significant cell type-disease associations, including two with type 1 diabetes, two with Crohns disease, and one with ulcerative colitis. Together, our results demonstrate that cell type proportions observed in scRNA-seq can reveal regulatory loci and offer insights into how genetic variations regulate immune cell type proportions to affect disease risk. Although we focused on immune single-cell data, our framework is applicable to other tissues or cellular compositions as scRNA-seq datasets expand. Author SummaryGenome-wide association studies (GWASs) have uncovered many disease-associated signals, yet most lie in noncoding regions and are difficult to interpret. Mapping GWAS signals to the relevant cell types is therefore important for better understanding the biological mechanisms that drive disease. A major challenge is that observed gene expression and measured cell-type proportions can be influenced by environmental factors and disease status. In contrast, genotypes are less affected by these factors, making them more reliable for interpreting factors of diseases. Moreover, the cell-type proportions are bounded and often skewed, so standard GWAS models that rely on Gaussian assumptions may lose power. To address this, we developed a quasi-binomial approach that better matches the data and improves discovery while controlling false positives. In real data, our method identified more genetic loci associated with cell-type proportions than a traditional linear model. To further investigate how genetic variation regulates immune cell composition to influence disease risk, we integrated our results with disease GWAS summary statistics to identify immune cell types that may contribute to disease susceptibility. Together, our results link disease-associated GWAS signals to specific immune cell types and provide insights into the cellular mechanisms that may underlie these diseases.